Sampling Weights

Hey. This is Brian from the RSC. I recently had to opportunity to do some work with the NHANES (National Health and Nutrition Examination Survey) data provided through the Centers for Disease Control and Prevention (CDC). One of the big issues with large scale surveys, like the NHANES, is understanding sampling weights. This post will take the time to review the basic concept of a sampling weight. Following posts will discuss weighting in NHANES.

Sampling weights are defined as the reciprocal of the probability of selection (which is n/N) where N is the total population size and n is the sample size (Lohr, 1999). Therefore a sampling weight is defined as (N/n).

Let's consider a simple example of this. If we have a county with 5000 people (N = 5000) and we administer a survey to a sample of 100 people (n = 100), the probability of being selected is 100/5000 or .02. If you lived in this county, you would have a 2% chance of being randomly selected for the survey (probability of selection). My sampling weight is the reciprocal of that probability (1/.02) which is 50. This essentially means that my answer to the survey is "worth 50 people" within the county. The weight shows how much I represent.

This weight is important because the sum of the weights equals the total population. Returning to our example, 100 people each with a weight of 50 people equals 5000 people. So in the NHANES, the sum of the weights should equal the population of interest (e.g., the adult population of the United States).

Stay tuned for more on sampling weights in NHANES.


* Sharon Lohr, Sampling: Design and Analysis, 1st edition